water resources in a changing climate: nsf espcor vi hydroclimatology v. sridhar, xin jin, david...
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Water Resources in a Changing Climate: NSF ESPCoR VI Hydroclimatology
V. Sridhar, Xin Jin, David Hoekema, Sumathy Sinnathamby, Muluken Muche
R. Allen, Wenguang ZhaoM. Germino
Background and Context
Region-wide warming Precipitation change Decline of snowpack Earlier spring runoff and Decline in summer streamflow trends
Research Questions
How will future climate change impact water resources?
Hydro - Climate
Hydro - Economic / Policy
Hydro - Ecology
Research Questions
Hydro-climate interactions
What are the relationships between climate change, vegetation, snow pack, and the resulting stream flows in managed and unmanaged river systems?
How will aquifer systems exchange with surface and
groundwater under various climate change scenarios?
What will both the supply and demand on water be in these systems under various climate change scenarios?
How will fire and invasive species (cheat grass, some bunchgrasses) impact:
Rates and durations of ET fluxes from desert systems?
Changes in infiltration patterns for precipitation?
Interactions of ET, infiltration, thermal profiles and microbial populations and feedbacks?
Erosion and Sedimentation in Tributaries of the Snake and Salmon basins?
Hydroclimate Bio Interactions
Focus Area 2009-2011
Research 1: Advance our ability to model surface energy balance processes
Tasks: Use of scintillometer and eddy covariance (EC) systems to measure sensible (H) and latent (LE) heat fluxes in desert and timber
Research 2:Advancement in Basin scale Hydrologic Forecasting – verification and operation under climate change scenarios
Task1 : Operate, test and calibrate the CIG Variable Infiltration Capacity (VIC) model and combine with the groundwater model
Task2: Implement VIC –groundwater model and evaluate the reservoir optimization techniques
Scintillometry
Use to Retrieve Sensible Heat Flux (H) over large, integrated transects
Use to improve components in METRIC and VIC Reduce variances
Use to derive ET experimentally Determine how soil water meters out from desert
and lodgepole
Transmitter
Receiver
Surface Energy Balance Processes----Large aperture scintillometer--transmitter (left) and 3 intercompared receivers (right) purchased by Idaho EPSCoR RII
Scintillometers to measure surface heat flux densities
Scintillometer – Idaho RII Deployments
Three systems (BSU, ISU, UI)
Three deployments Sage brush -- Snake River Plain
Cheat grass or recent burn – Snake River Plain
Timber – Upper Reaches
Idaho RII: Sagebrush Deployment
Sage brush ecosystem located west of Hollister, Idaho.
Raft River Site Cheat grass
LodgePole Pine Site --- Macks Inn Area
Scintillometers measure only Sensible Heat flux (H)
ET is calculated as a residual of the energy balance
ET = Rn – G – H
Net radiation, Rn, and soil heat flux, G, must be measured
All error in Rn, G and H transfer into ET
Preliminary VIC model calibration & Flowchart for sensitivity analysis
Choose parameters to be calibrated. In this study, 7 parameters were chosen recommendation
Construct nxp Jacobian matrix and
calculate CSSj
Identify the most sensitive parameters (with the biggest CSSj) end
Choose observation locations to be used as reference. In this study, 6 locations were chosen
Calculate sensitivity for each observation yi (i=1,…,n) over each
parameter bj (j=1,…,p).
Divide the range of each parameter into equal space
Run VIC and routing with one parameter changed and the others unchanged. Obtaining new RMSE.
Divide the Snake River Basin into 6 sub-basins and select the new set of parameters that make the RMSE minimum and apply it to the VIC
Ws Ts3 binf Ts2 Ds Dsmax Ts1
2.54×105 2.00×105 1.67×105 1.48×105 1.11×105 1.08×105 4.87×104
Parameter Dsmax Ds binf Ws Ts1, Ts2, Ts3
Unit mm/day % NA % mRange >0 to ~30 >0 to 1 >0 to ~0.4 >0 to 1 0.1 – 1.5
• Ws fraction of maximum soil moisture where non-linear baseflow occurs
VIC model calibration results (all 6 locations, 1928 - 1978)
RMSE (cfs) r2
uncalibrated calibrated uncalibrated calibrated
Heise 3582 2427 0.88 0.90
Rexburg 2168 1683 0.87 0.89
Milner 5649 4708 0.85 0.86
Oxbow 15232 12099 0.90 0.85
Parma 3127 1837 0.75 0.81
Payette 2603 1532 0.86 0.88
VIC model validation results (all 6 locations, 1979 - 2005)
RMSE (cfs) r2
uncalibrated calibrated uncalibrated calibrated
Heise 3462 2556 0.92 0.90
Rexburg 2158 1844 0.87 0.83
Milner 5400 4871 0.87 0.86
Oxbow 14026 12796 0.92 0.86
Parma 2639 1687 0.77 0.83
Payette 1988 1859 0.88 0.88
VIC model calibration results (at Heise)
Default calibrated from University of Washington
calibrated
Preliminary VIC results (1979-2004)
Parameter Selection for the SWAT ModelSnowmelt and snow formation parameter
Ground water parameter
Soil parameter
Surface Runoff parameter
SWAT Calibration and validation
Salmon River Snake River
Optimization objective functions
White bird Krassel Ranger
Yellowpine Millner Oxbow
Nash -Sutcliffe model efficiency (Ens) ∑∑==
−−−=n
iobsobssim
n
iobs QmeanQQQ
1
22
1
))((/)(1
Calibration Validation
0.57 0.49
0.25 0.23
0.47 0.49
0.52 0.46
0.39 0.40
Percent bias (PBIAS)
=
∑ ∑= =
−n
i
n
iQobssimobs QQ
1 1
/)(
Calibration Validation
0.04 0.03
0.11 0.07
0.2 0.14
0.06 0.05
0.07 0.08
Regression coefficient R 2 Calibration Validation
0.57 0.60
0.53 0.55
0.63 0.51
0.53 0.46
0.42 0.42
Where Qobs is the measured monthly s treamflow, Qsim is the simulated monthly streamflow, mean (Qobs) is the mean of the measured monthly streamflow, and n is the number of measurement.
Results
PrecipitationMaximum
TemperatureMinimum
Temperature PrecipitationMaximum
TemperatureMinimum
TemperatureA1B + + + + + +
ECHAM A2 - + + - + +B1 + - - + + +
A1B + + + + + +GISS A2 - + + + + +
B1 - + + + + +A1B + + + + + +
IPSL A2 + + + + + +B1 + + + + + +
Model Scenario
Future trendSnake River watershedSalmon River Watershed
Decreasing trend in monthly discharges
Salmon River watershed Snake River watershed
White bird Krassel Ranger Yellowpine
ECHAM GISS IPSL
A1b
A2
B1
A1b
A2
B1
A1b
A2
B1
Oxbow Milner
ECHAM
GISS
IPSL
A1b
A2
B1
A1b
A2
B1
Oxbow Milner
Snake River watershedA1b
A2
B1
A1b
A2
B1
GUI development of ESPAMGUI development of ESPAM
GUI development of ESPAMGUI development of ESPAM
Flow Distribution & Points of Interest (POI)Six Points of Interest
1) Heise (Snake River)
2) Rexburg (Henry’s Fork)
3) Milner (Snake River)
4) Parma (Boise River)
5) Payette (Payette River)
6) Oxbow (Snake River)
58%
+32%
90 % Total
Points of interest were chosen from which projected flows could be distributed to simulate upstream reach gain contributions. As represented in the chart below, we selected six points of interest that cover 90% of the flow in the upper SRB.
1) Monthly Natural flow (sum of upstream reaches)
Where,
NFm = monthly natural flow at reach d
d = downstream reach (or point of interest)
u = furthest upstream reach
xi = any given reach between u and d
• Annual Natural Flow (sum of monthly Natural Flows)
Where, NFm,1 = natural monthly flow in October, NFM,2 = natural monthly flow in November….
Reach Gain Simulation Calculations
∑=
+++=u
diu
xi
xd
x )....(m
NF
∑=
=12
1,y
NF
iim
NF
The first step of the reach gain simulation method is to categorize flow based on a range of historic annual natural flows. The equations for calculating natural flow from IDWR historic reach gains are presented here.
Flow Categorization Henry’s Fork
Flow Range per Category: 3000 (100 acre-feet) Minimum: 13678Maximum: 40697Mean: 24768
Dry < 180001 18000 210002 21000 240003 24000 270004 27000 300005 30000 33000
Wet > 33000Flow categorization is based on annual flows while simulation of these flows are based on monthly distributions of the projected flow. Along the Henry’s Fork flows are categorized with a range of 300,000 acre-feet per category.
Predicting Minor Flows—Linear Model
Flow W. = (2.18*(%Avg. Flow Ox.) - 1.18)*W. Avg.
Model Validation
Irrigation Shortage Comparison: Historic vs. Simulated (1980-2005)
A comparison between SRPM calculated irrigation shortages as represented by historic and simulated reach gains reveals that the reach gain simulation method was able to provide perfect replication of historic irrigation shortages in the river between the years 1980 and 2005.
Falls Teton Henry's Above Lorenzo Willow Blkft Blkft Milner Boise NewY Payett
River River Fork Lornezo Blkft Creek Prtnf Milner Murphy River Canal River TOTAL
His. 0.3% 0.9% 0.6% 0.6% 15.0% 0.4% 49.0% 36.2% 0.0% 27.8% 74.8% 14.6% 20.7%
Sim. 3.8% 6.0% 8.3% 0.5% 21.8% 3.3% 47.6% 56.8% 0.0% 43.3% 87.1% 15.3% 30.3%∆Shortage 3.5% 5.1% 7.8% 0.1% 6.8% 2.9% 1.4% 20.6% 0.0% 15.5% 12.3% 0.7% 9.5%
Payette Watershed-Future Climate: Echam-5
Deadwood Dam
Cascade Dam
Black Canyon Dam
Future Climate Payette River
Future Climate Payette River
Future Shortages Payette River
VIC+MODFLOW flowchartVIC+MODFLOW flowchart
Run VIC model to generate the infiltration, evapotransporation
(ET), runoff and baseflow at each cell of unsaturated zone
Run MODFLOW and generate recharge, water content
Infiltration, ET
Add a fraction of recharge from MODFLOW the baseflow in VIC output
Run VIC routing model
No
Yes
Stop
Reaching time step limit
Water content
END
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